# An Explainable AI Exploration of the Machine Learning Classification of Neoplastic Intracerebral Hemorrhage from Non-Contrast CT

## TL;DR

This study uses explainable AI to understand how a machine learning model distinguishes between cancer-related and non-cancer-related brain hemorrhages using CT scans.

## Contribution

The study introduces a novel application of explainable AI to analyze the decision-making of a deep-learning model in classifying neoplastic intracerebral hemorrhage.

## Key findings

- The model relies more on features within the hemorrhage than in surrounding edema.
- ICH importance was on average 30% higher than PHE importance in model predictions.
- Significant differences in ICH importance were observed between neoplastic and non-neoplastic cases.

## Abstract

This study investigates which imaging features a deep-learning model uses to distinguish between neoplastic and non-neoplastic brain hemorrhages. Explainable artificial intelligence techniques show that the model relies primarily on features in the hemorrhage, but also considers features in the surrounding edema.

Objective: To understand the importance of different imaging features in the automatic classification of neoplastic and non-neoplastic intracerebral hemorrhage (ICH) using admission CT. Methods: This study builds on a previously published machine learning model for the classification of neoplastic vs. non-neoplastic ICH. In the current work, we analyzed its decision process with explainable AI methods. We compared the average importance of ICH and perihematomal edema (PHE) in the model’s predictions to gain insight into its decision process regarding the etiology classification. The model predictions were explained using various image-based explanation methods, and the best method was selected based on the faithfulness metric. Results: The study population consisted of 349 cases (144 neoplastic, 205 non-neoplastic; median age 67, 167 female). The best explanation method according to the faithfulness metric was GradCam++. Both the ICH and PHE regions were important for the classification. The ICH importance was on average 30% higher compared to the PHE importance (p < 0.001). Further, there was a significant difference between the importance of ICH in neoplastic vs. non-neoplastic cases (p < 0.001) which was on average 7.3% higher. A subgroup analysis showed a significant difference between the two classes for the PHE region for lesions smaller (p = 0.02) and larger than the median ICH volume (p = 0.001), but not for the full study population (p = 0.54). Conclusions: Our results confirm the importance of PHE in the classification of neoplastic ICH but show that the ICH region remains of higher importance.